7,606 research outputs found

    Application of a rule-based knowledge system using CLIPS for the taxonomy of selected Opuntia species

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    A rule based knowledge system was developed in CLIPS (C Language Integrated Production System) for identifying Opuntia species in the family Cactaceae, which contains approx. 1500 different species. This botanist expert tool system is capable of identifying selected Opuntia plants from the family level down to the species level when given some basic characteristics of the plants. Many plants are becoming of increasing importance because of their nutrition and human health potential, especially in the treatment of diabetes mellitus. The expert tool system described can be extremely useful in an unequivocal identification of many useful Opuntia species

    Simple identification tools in FishBase

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    Simple identification tools for fish species were included in the FishBase information system from its inception. Early tools made use of the relational model and characters like fin ray meristics. Soon pictures and drawings were added as a further help, similar to a field guide. Later came the computerization of existing dichotomous keys, again in combination with pictures and other information, and the ability to restrict possible species by country, area, or taxonomic group. Today, www.FishBase.org offers four different ways to identify species. This paper describes these tools with their advantages and disadvantages, and suggests various options for further development. It explores the possibility of a holistic and integrated computeraided strategy

    Automated pre-processing strategies for species occurrence data used in biodiversity modelling

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    To construct Biodiversity richness maps from Environmental Niche Models (ENMs) of thousands of species is time consuming. A separate species occurrence data pre-processing phase enables the experimenter to control test AUC score variance due to species dataset size. Besides, removing duplicate occurrences and points with missing environmental data, we discuss the need for coordinate precision, wide dispersion, temporal and synonymity filters. After species data filtering, the final task of a pre-processing phase should be the automatic generation of species occurrence datasets which can then be directly ’plugged-in’ to the ENM. A software application capable of carrying out all these tasks will be a valuable time-saver particularly for large scale biodiversity studies

    A look inside the Pl@ntNet experience

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    International audiencePl@ntNet is an innovative participatory sensing platform relying on image-based plants identification as a mean to enlist non-expert contributors and facilitate the production of botanical observation data. One year after the public launch of the mobile application, we carry out a self-critical evaluation of the experience with regard to the requirements of a sustainable and effective ecological surveillance tool. We first demonstrate the attractiveness of the developed multimedia system (with more than 90K end-users) and the nice self-improving capacities of the whole collaborative workflow. We then point out the current limitations of the approach towards producing timely and accurate distribution maps of plants at a very large scale. We discuss in particular two main issues: the bias and the incompleteness of the produced data. We finally open new perspectives and describe upcoming realizations towards bridging these gaps

    The taxonomic name resolution service : an online tool for automated standardization of plant names

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    © The Author(s), 2013. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in BMC Bioinformatics 14 (2013): 16, doi:10.1186/1471-2105-14-16.The digitization of biodiversity data is leading to the widespread application of taxon names that are superfluous, ambiguous or incorrect, resulting in mismatched records and inflated species numbers. The ultimate consequences of misspelled names and bad taxonomy are erroneous scientific conclusions and faulty policy decisions. The lack of tools for correcting this ‘names problem’ has become a fundamental obstacle to integrating disparate data sources and advancing the progress of biodiversity science. The TNRS, or Taxonomic Name Resolution Service, is an online application for automated and user-supervised standardization of plant scientific names. The TNRS builds upon and extends existing open-source applications for name parsing and fuzzy matching. Names are standardized against multiple reference taxonomies, including the Missouri Botanical Garden's Tropicos database. Capable of processing thousands of names in a single operation, the TNRS parses and corrects misspelled names and authorities, standardizes variant spellings, and converts nomenclatural synonyms to accepted names. Family names can be included to increase match accuracy and resolve many types of homonyms. Partial matching of higher taxa combined with extraction of annotations, accession numbers and morphospecies allows the TNRS to standardize taxonomy across a broad range of active and legacy datasets. We show how the TNRS can resolve many forms of taxonomic semantic heterogeneity, correct spelling errors and eliminate spurious names. As a result, the TNRS can aid the integration of disparate biological datasets. Although the TNRS was developed to aid in standardizing plant names, its underlying algorithms and design can be extended to all organisms and nomenclatural codes. The TNRS is accessible via a web interface at http://tnrs.iplantcollaborative.org/ webcite and as a RESTful web service and application programming interface. Source code is available at https://github.com/iPlantCollaborativeOpenSource/TNRS/ webcite.BJE was supported by NSF grant DBI 0850373 and TR by CSIRO Marine and Atmospheric Research, Australia,. BB and BJE acknowledge early financial support from Conservation International and TEAM who funded the development of early prototypes of taxonomic name resolution. The iPlant Collaborative (http://www.iplantcollaborative.org) is funded by a grant from the National Science Foundation (#DBI-0735191)

    Next-Generation Field Guides

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    To conserve species, we must first identify them. Field researchers, land managers, educators, and citizen scientists need up-to-date and accessible tools to identify organisms, organize data, and share observations. Emerging technologies complement traditional, book-form field guides by providing users with a wealth of multimedia data. We review technical innovations of next-generation field guides, including Web-based and stand-alone applications, interactive multiple-access keys, visual-recognition software adapted to identify organisms, species checklists that can be customized to particular sites, online communities in which people share species observations, and the use of crowdsourced data to refine machine-based identification algorithms. Next-generation field guides are user friendly; permit quality control and the revision of data; are scalable to accommodate burgeoning data; protect content and privacy while allowing broad public access; and are adaptable to ever-changing platforms and browsers. These tools have great potential to engage new audiences while fostering rigorous science and an appreciation for nature.Organismic and Evolutionary Biolog

    A new method for creating a visual plant identification key

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    Taxonomic keys are essential tools for species identification, used by students and professional biologists. In recent years, advancements in photography have allowed these keys to host high-quality photographs for aid in identification. However, most modern keys still rely heavily on text rather than images. Using text alone limits the user to a discrete number of characters, often described in esoteric terms. In order to create more effective keys, we developed a new method for constructing image-based taxonomic keys. These keys replace written characters with images – allowing the user to identify species using visual pattern recognition, rather than interpreting written text. In addition, we constructed our visual key using data on how different users assess the visual similarities between plant species. To ensure the strength of this methodology, our key focuses on the morphologically diverse genus, Quercus. A set of standardized photographs was taken of forty-three species of oak native or naturalized in the Southeast. These photographs were used to create a survey on how botanical experts and botanical novices rate the pair-wise similarity of different oak leaves. The mean of each rating was summarized into a distance matrix, which was then converted into a dendrogram. From the resulting dendrogram, a visual key was constructed using the standardized photographs of oak leaves. The key was then tested on against an existing dichotomous key using botanical novices and botanical experts. The resulting two-sample t-tests between the two identification keys demonstrated that users with our visual key produced between 22-30% more correct answers than users with the traditional key. Using this method of key creation, innovative keys could be constructed for other fields of biology
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